Current FDA post-marketing drug safety monitoring relies principally on passive surveillance via MedWatch reports to the Adverse Event Report System. By contrast, active surveillance using population-based healthcare utilization databases could improve the validity and timeliness of signal detection. FDA's proposed Sentinel Initiative plans to utilize healthcare databases and their research environments for ongoing drug safety monitoring and highlights the importance of developing the capability to conduct active safety surveillance for signal generation and confirmation. However, much needs to be learned about how to implement and utilize such data. Signals are stronger-than-expected associations between a drug and adverse medical events. One common approach is to routinely accumulate drug exposure and medical event data as they become available (e.g. monthly) and regularly evaluate this cumulative cohort study. Despite the enormous potential public health benefit of ongoing drug safety monitoring, there is little understanding of when such a prospective cumulative monitoring activity should be stopped to warn the public, or to conclude that a medication is safe. Delaying such a decision could unnecessarily put patients at risk, but, conversely, falsely warning of a risk may reduce use of important medications. Both scenarios may result in increased morbidity and/or mortality as a consequence of inadequate decision making. Based on 25 years of experience with longitudinal claims databases and an ongoing close collaboration with WellPoint/HealthCore, a collection of 14 BlueCross health plans that records data on all drug dispensing and healthcare utilization for 30 million insured members, we propose to empirically evaluate published stopping rules and develop and test new algorithms to address this pressing question. Specifically, we will: --- Identify, characterize, and build empirical example studies for drug safety monitoring and develop a set of simulated data sources, --- Develop and apply a set of "stopping rules" for the data scenarios identified above that would terminate a cumulative cohort study based on epidemiologic and clinical metrics and sequential test statistics, --- Using a decision analytic framework we will combine the epidemiologic stopping rules above with information on the societal cost of false positive and false negative findings, --- SAS macros for all tested approaches will be deposited on a web-page. This 2-year project will greatly increase our understanding of cumulative drug safety monitoring based on empirical and simulated data and in combination with a decision analytic framework, leading to the development of enhanced, data-driven stopping rules for use with emerging pharmacoepidemiologic databases.